/***********************************************************************
This file is part of KEEL-software, the Data Mining tool for regression,
classification, clustering, pattern mining and so on.
Copyright (C) 2004-2010
F. Herrera (herrera@decsai.ugr.es)
L. S�nchez (luciano@uniovi.es)
J. Alcal�-Fdez (jalcala@decsai.ugr.es)
S. Garc�a (sglopez@ujaen.es)
A. Fern�ndez (alberto.fernandez@ujaen.es)
J. Luengo (julianlm@decsai.ugr.es)
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see http://www.gnu.org/licenses/
**********************************************************************/
/**
*
* File: GGA.java
*
* Population Based Incremental Learning for Instance Selection.
*
* @author Written by Salvador Garc�a (University of Granada) 20/07/2004
* @version 0.1
* @since JDK1.5
*
*/
package keel.Algorithms.Instance_Selection.PBIL;
import keel.Algorithms.Preprocess.Basic.*;
import org.core.*;
import java.util.StringTokenizer;
import java.util.Arrays;
public class PBIL extends Metodo {
/*Own parameters of the algorithm*/
private long semilla;
private int generations;
private int tamPoblacion;
private double LR;
private double mutShift;
private double pMutation;
private double negativeLR;
private double alfa;
private double initialValuePV;
private int kNeigh;
/**
* Default builder. Construct the algoritm by using the superclass builder.
*
*/
public PBIL (String ficheroScript) {
super (ficheroScript);
}//end-method
/**
* Executes the algorithm
*/
public void ejecutar () {
int i, j, l;
int nClases;
double conjS[][];
double conjR[][];
int conjN[][];
boolean conjM[][];
int clasesS[];
int nSel = 0;
Cromosoma poblacion[];
double PV[];
Cromosoma best;
double mejCal, badCal;
int mejor, peor;
double u;
int ev = 0;
long tiempo = System.currentTimeMillis();
/*Getting the number of different classes*/
nClases = 0;
for (i=0; i<clasesTrain.length; i++)
if (clasesTrain[i] > nClases)
nClases = clasesTrain[i];
nClases++;
/*Random inicialization of the poblation*/
Randomize.setSeed (semilla);
poblacion = new Cromosoma[tamPoblacion];
PV = new double[datosTrain.length];
Arrays.fill(PV,initialValuePV);
for (i=0; i<tamPoblacion; i++)
poblacion[i] = new Cromosoma (datosTrain.length);
best = new Cromosoma (datosTrain.length,poblacion[0]);
/*Initial evaluation of the poblation*/
for (i=0; i<tamPoblacion; i++) {
poblacion[i].evalua(datosTrain, realTrain, nominalTrain, nulosTrain, clasesTrain, alfa, kNeigh, nClases, distanceEu);
ev++;
}
mejCal = 0; mejor = 0;
badCal = 100; peor = 0;
/*Search for the best solution*/
for (i=0; i<tamPoblacion; i++)
if (poblacion[i].getCalidad() > mejCal) {
mejor = i;
mejCal = poblacion[i].getCalidad();
if (mejCal > best.getCalidad() ){
best = new Cromosoma (datosTrain.length, poblacion[mejor]);
}
} else if (poblacion[i].getCalidad() < badCal) {
peor = i;
badCal = poblacion[i].getCalidad();
}
/*Update of the probabilities vector*/
for (i=0; i<datosTrain.length; i++) {
if (poblacion[mejor].getGen(i) != poblacion[peor].getGen(i)) {
if (poblacion[mejor].getGen(i))
PV[i] = PV[i]*(1-negativeLR)+ negativeLR;
else
PV[i] = PV[i]*(1-negativeLR);
} else {
if (poblacion[mejor].getGen(i))
PV[i] = PV[i]*(1-LR)+LR;
else
PV[i] = PV[i]*(1-LR);
}
/*Mutation of the probabilities vector*/
u = Randomize.Rand();
if (u < pMutation) {
PV[i]= PV[i]* (1-mutShift) + (Randomize.Randint(0,1))*mutShift;
}
}
while (ev < generations) {
/*Getting a new poblation*/
for (i=0; i<tamPoblacion; i++)
poblacion[i] = new Cromosoma (datosTrain.length, PV);
/*Evaluation of the poblation*/
for (i=0; i<tamPoblacion; i++) {
poblacion[i].evalua(datosTrain, realTrain, nominalTrain, nulosTrain, clasesTrain, alfa, kNeigh, nClases, distanceEu);
ev++;
}
mejCal = 0;
badCal = 100;
/*Search the best solution*/
for (i=0; i<tamPoblacion; i++)
if (poblacion[i].getCalidad() > mejCal) {
mejor = i;
mejCal = poblacion[i].getCalidad();
if (mejCal > best.getCalidad() ){
best = new Cromosoma (datosTrain.length, poblacion[mejor]);
}
} else if (poblacion[i].getCalidad() < badCal) {
peor = i;
badCal = poblacion[i].getCalidad();
}
/*Update of the probabilities vector*/
for (i=0; i<datosTrain.length; i++) {
if (poblacion[mejor].getGen(i) != poblacion[peor].getGen(i)) {
if (poblacion[mejor].getGen(i))
PV[i] = PV[i]*(1-negativeLR)+ negativeLR;
else
PV[i] = PV[i]*(1-negativeLR);
} else {
if (poblacion[mejor].getGen(i))
PV[i] = PV[i]*(1-LR)+LR;
else
PV[i] = PV[i]*(1-LR);
}
/*Mutation of the probabilities vector*/
u = Randomize.Rand();
if (u < pMutation) {
PV[i]= PV[i]* (1-mutShift) + (Randomize.Randint(0,1))*mutShift;
}
}
}
nSel = best.genesActivos();
/*Construction of S set from the best cromosome*/
conjS = new double[nSel][datosTrain[0].length];
conjR = new double[nSel][datosTrain[0].length];
conjN = new int[nSel][datosTrain[0].length];
conjM = new boolean[nSel][datosTrain[0].length];
clasesS = new int[nSel];
for (i=0, l=0; i<datosTrain.length; i++) {
if (best.getGen(i)) { //the instance must be copied to the solution
for (j=0; j<datosTrain[i].length; j++) {
conjS[l][j] = datosTrain[i][j];
conjR[l][j] = realTrain[i][j];
conjN[l][j] = nominalTrain[i][j];
conjM[l][j] = nulosTrain[i][j];
}
clasesS[l] = clasesTrain[i];
l++;
}
}
System.out.println("PBIL "+ relation + " " + (double)(System.currentTimeMillis()-tiempo)/1000.0 + "s");
// COn conjS me vale.
int trainRealClass[][];
int trainPrediction[][];
trainRealClass = new int[datosTrain.length][1];
trainPrediction = new int[datosTrain.length][1];
//Working on training
for ( i=0; i<datosTrain.length; i++) {
trainRealClass[i][0] = clasesTrain[i];
trainPrediction[i][0] = KNN.evaluate(datosTrain[i],conjS, nClases, clasesS, this.kNeigh);
}
KNN.writeOutput(ficheroSalida[0], trainRealClass, trainPrediction, entradas, salida, relation);
//Working on test
int realClass[][] = new int[datosTest.length][1];
int prediction[][] = new int[datosTest.length][1];
//Check time
for (i=0; i<realClass.length; i++) {
realClass[i][0] = clasesTest[i];
prediction[i][0]= KNN.evaluate(datosTest[i],conjS, nClases, clasesS, this.kNeigh);
}
KNN.writeOutput(ficheroSalida[1], realClass, prediction, entradas, salida, relation);
}//end-method
/**
* Reads configuration script, and extracts its contents.
*
* @param ficheroScript Name of the configuration script
*
*/
public void leerConfiguracion (String ficheroScript) {
String fichero, linea, token;
StringTokenizer lineasFichero, tokens;
byte line[];
int i, j;
ficheroSalida = new String[2];
fichero = Fichero.leeFichero (ficheroScript);
lineasFichero = new StringTokenizer (fichero,"\n\r");
lineasFichero.nextToken();
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
token = tokens.nextToken();
/*Getting the name of training and test files*/
line = token.getBytes();
for (i=0; line[i]!='\"'; i++);
i++;
for (j=i; line[j]!='\"'; j++);
ficheroTraining = new String (line,i,j-i);
for (i=j+1; line[i]!='\"'; i++);
i++;
for (j=i; line[j]!='\"'; j++);
ficheroValidation = new String (line,i,j-i);
for (i=j+1; line[i]!='\"'; i++);
i++;
for (j=i; line[j]!='\"'; j++);
ficheroTest = new String (line,i,j-i);
/*Obtaining the path and the base name of the results files*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
token = tokens.nextToken();
/*Getting the name of output files*/
line = token.getBytes();
for (i=0; line[i]!='\"'; i++);
i++;
for (j=i; line[j]!='\"'; j++);
ficheroSalida[0] = new String (line,i,j-i);
for (i=j+1; line[i]!='\"'; i++);
i++;
for (j=i; line[j]!='\"'; j++);
ficheroSalida[1] = new String (line,i,j-i);
/*Getting the seed*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
semilla = Long.parseLong(tokens.nextToken().substring(1));
/*Getting the number of generations*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
generations = Integer.parseInt(tokens.nextToken().substring(1));
/*Getting the size of the poblation*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
tamPoblacion = Integer.parseInt(tokens.nextToken().substring(1));
/*Getting the LR tase*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
LR = Double.parseDouble(tokens.nextToken().substring(1));
/*Getting the mutation shift*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
mutShift = Double.parseDouble(tokens.nextToken().substring(1));
/*Getting the probability of mutation*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
pMutation = Double.parseDouble(tokens.nextToken().substring(1));
/*Getting the Negative LR tase*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
negativeLR = Double.parseDouble(tokens.nextToken().substring(1));
/*Getting the alpha equilibrate factor*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
alfa = Double.parseDouble(tokens.nextToken().substring(1));
/*Getting the alpha equilibrate factor*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
initialValuePV = Double.parseDouble(tokens.nextToken().substring(1));
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
kNeigh = Integer.parseInt(tokens.nextToken().substring(1));
/*Getting the type of distance function*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
distanceEu = tokens.nextToken().substring(1).equalsIgnoreCase("Euclidean")?true:false;
}//end-method
}//end-class